Modern applications built on Firestore and Cloud Functions work beautifully for transactional workloads—but what happens when your business needs to answer deeper, relationship-driven questions? Questions like “Who influences purchases in my network?”, “Which users are connected in a fraud ring?”, or “What do similar users in Noida prefer to buy?” In this session, Manjunath Janardhan takes you through a story from Noida’s e-commerce ecosystem and shows how traditional Firestore queries start to struggle as relationship depth grows. You’ll explore when and why graph databases shine, how they complement Firestore, and how you can use polyglot persistence on GCP to get the best of both worlds. We’ll walk through practical architecture patterns, performance comparisons, and live demos featuring: 1. Firestore vs Graph DB query performance 2. Multi-hop traversals, pattern detection, and recommendations 3. Vertex AI–powered natural language → Cypher query generation 4. A real demo app using Cloud Functions, Firestore, Neo4j, and Vertex AI If your GCP application is hitting limits with complex joins, recommendation logic, fraud detection, or influence analysis—this session will show you a cleaner, faster, future-proof approach. Come learn how to make your GCP applications understand relationships, not fight them!